Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Physical database design decision algorithms and concurrent reorganization for parallel database systems
The dawning of the autonomic computing era
IBM Systems Journal
Autonomous Query-Driven Index Tuning
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Automatic physical database tuning: a relaxation-based approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
COLT: continuous on-line tuning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient use of the query optimizer for automated physical design
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Compressing Very Large Database Workloads for Continuous Online Index Selection
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
A genetic algorithm for the index selection problem
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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In recent years, many algorithms for automatic physical database tuning have been proposed and successfully used in tools for administration of relational database management systems. The novel method described in this paper uses a steady-state evolutionary approach to continuously give index recommendations so that the database management system can adapt to changing workload and data distribution. Contrary to online algorithms offering recommendations on a per-query basis, our solution takes into account index reuse accross different queries. The experiments show that the quality of the recommendations obtained by the proposed method matches the quality of recommendations given by the best offline index selection algorithms. Moreover, high performance and low memory footprint of the method make it suitable for autonomic database tuning systems.